Prevalence of undiagnosed stage 3 chronic kidney disease in France, Germany, Italy, Japan and the USA: results from the multinational observational REVEAL-CKD study
Bibliographic record
Abstract
OBJECTIVES: REVEAL-CKD aims to estimate the prevalence of, and factors associated with, undiagnosed stage 3 chronic kidney disease (CKD). DESIGN: Multinational, observational study. SETTING: Data from six country-specific electronic medical records and/or insurance claims databases from five countries (France, Germany, Italy, Japan and the USA [two databases]). PARTICIPANTS: ). Undiagnosed cases lacked an International Classification of Diseases 9/10 diagnosis code for CKD (any stage) any time before, and up to 6 months after, the second qualifying eGFR measurement (study index). MAIN OUTCOME MEASURES: The primary outcome was point prevalence of undiagnosed stage 3 CKD. Time to diagnosis was assessed using the Kaplan-Meier approach. Factors associated with lacking a CKD diagnosis and risk of diagnostic delay were assessed using logistic regression adjusted for baseline covariates. RESULTS: The prevalence of undiagnosed stage 3 CKD was 95.5% (19 120/20 012 patients) in France, 84.3% (22 557/26 767) in Germany, 77.0% (50 547/65 676) in Italy, 92.1% (83 693/90 902) in Japan, 61.6% (13 845/22 470) in the US Explorys Linked Claims and Electronic Medical Records Data database and 64.3% (161 254/250 879) in the US TriNetX database. The prevalence of undiagnosed CKD increased with age. Factors associated with undiagnosed CKD were female sex (vs male, range of odds ratios across countries: 1.29-1.77), stage 3a CKD (vs 3b, 1.81-3.66), no medical history (vs a history) of diabetes (1.26-2.77) or hypertension (1.35-1.78). CONCLUSIONS: There are substantial opportunities to improve stage 3 CKD diagnosis, particularly in female patients and older patients. The low diagnosis rates in patients with comorbidities that put them at risk of disease progression and complications require attention. TRIAL REGISTRATION: NCT04847531.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".